Knowledge of locations of users and devices inside a building is an important prerequisite for location-based services and aspects of ubiquitous computing. One promising approach to determining location is through triangulation by measuring IEEE 802.11 wireless signal strengths of wireless devices. One of the most attractive features of an IEEE 802.11 location-based system is that it does not require any extra infrastructure beyond a wireless network that already exists in many buildings. This is in contrast to other person-tracking systems that employ active/passive badges and cameras, which in turn require installation and maintenance of extra equipment.
Using an 802.11 wireless client as a location sensor (e.g., a portable computer as a receiver) is becoming increasingly popular way of enabling location-based services. Triangulation of signal strengths from multiple access points (APs) may be used to pinpoint location of the receiving device down to a few meters. However, this level of accuracy comes at a price of requiring tedious and time-consuming manual labor in order to obtain spatially high-density calibration data of signal strengths as a function of location.
Knowing radio signal strength measurements on a network client from a few different APs, researchers have shown how to compute location down to a few meters. This type of location measurement is especially attractive because it uses existing devices of a building and its users, and because it functions indoors where global positioning system (GPS) and cell phone location signals often break down. However, the accuracy of such systems usually depends on a meticulous calibration procedure that consists of physically moving a wireless client receiver to many disparate known locations, and different orientations, inside the building. It is often be impracticable to expect anyone to spend resources on such work—when presented with such prospect as part of a new product, software product planners often balk, complaining that system administrators are reluctant to even keep locations of printers updated, much less create and maintain a high-resolution table of IEEE 802.11 signal strengths.
One alternative to manual calibration is to analytically predict signal strengths based on a floor plan of a building, physical simulation of radio frequency (RF) propagation, and knowledge of the locations of wireless access points. It was discovered, for the chosen simulation method, that physically simulating signal strengths increased median location error by approximately 46% (from 2.94 meters to 4.3 meters) over values obtained by manual calibration. Moreover, a good physical simulation usually requires a more detailed model of the building than is normally available.
In the realm of IEEE 802.11 locations, one published work was based on the RADAR system, an in-building RF-based location and tracking system. RADAR worked based on a table of indoor locations and corresponding signal strengths. Using a manually calibrated table, the nearest neighbor algorithm gave a median spatial error of approximately 2.94 meters. Another table based on simulated radio wave propagation allowed the avoidance of most of the calibration work at the cost of increasing the median error to 4.3 meters. The RADAR work also looked at the problem of reducing calibration effort. It was found that reducing the number of calibration points from seventy to forty had only a small negative impact on accuracy. In follow-on work, RADAR was enhanced to use a Viterbi-like algorithm on short paths through the building. This further reduced the median error to approximately 2.37 meters.
As part of Carnegie Mellon's Andrew system, a limited study of an IEEE 802.11 location system was performed using eight discrete locations in a hallway. A table of signal strength versus location was built. It was determined that upon returning to the eight locations, the system inferred the right location 87.5% of the time.
Another location service used signal-to-noise ratios, instead of the more commonly used raw signal strengths. The location algorithm was a Bayesian network, manually trained at discrete locations in two buildings. The Bayes formulation allowed the inclusion of a priori probabilities of a person's location, as well as transition probabilities between locations. In one test on twelve locations in a hallway, the service was capable of identifying the highest probability to the correct location 97% of the time, not counting the 15% of the time it was inconclusive.
In still another study, IEEE 802.11 was used to compute the location of wireless PocketPCs, both indoors and outdoors. Instead of manual calibration, a formula was used that approximated the distance to a wireless access point as a function of signal strength. Using a hill-climbing algorithm, the system computes location down to about ten meters (approximately thirty-five feet) using signal strengths from multiple access points.
In yet another study of an IEEE 802.11 location system, Bayesian reasoning and a hidden Markov model were used. Not only were signal strengths taken into account, but also the probability of seeing an access point from a given location. Like other work, it was based upon a manual calibration. The system explicitly modeled orientation and achieved a median spatial error of approximately one meter using calibration samples taken approximately every 1.5 meters in hallways. Although in terms of accuracy, this may be perhaps the best result, the study also acknowledges the problem of calibration effort, and suggests that calibrated locations could be automatically inferred by outfitting the calibrator with an accelerometer and magnetic compass.
Some of the conventional systems described hereinabove are explicitly working toward more accuracy, but at the expense of increased calibration effort.